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Commit 344707bb authored by Jing Zhang's avatar Jing Zhang
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remove v5r1 nhwc

parent 17cd5c7d
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host/driver_offline/include/device_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk.hpp deleted 100644 → 0
View file @ 17cd5c7d
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_hi_wi_c,
const Tensor<TInWei>& wei_k_y_x_c,
Tensor<TOut>& out_n_ho_wo_k,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto N = out_n_ho_wo_k_lengths[I0];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto K = out_n_ho_wo_k_lengths[I3];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
const auto C = wei_k_y_x_c_lengths[I3];
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
DeviceMem out_n_ho_wo_k_device_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
in_n_hi_wi_c_device_buf.ToDevice(in_n_hi_wi_c.mData.data());
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(make_tuple(N, Ho, Wo, K));
#if 0
// cdata = 64, BlockSize = 64, 16x8x32x4
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 16;
constexpr index_t HoPerBlock = 16;
constexpr index_t WoPerBlock = 16;
constexpr index_t E1 = 4;
constexpr index_t E2 = 4;
constexpr index_t EPerBlock = 2;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K_E2 = Sequence<1, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K_E2 = Sequence<1, E1, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = 8;
#else
// cdata = 64, BlockSize = 64, 16x8x32x4
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 16;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t E1 = 4 * 9;
constexpr index_t E2 = 4;
constexpr index_t EPerBlock = 4;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K_E2 = Sequence<1, EPerBlock, 16, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = 8;
#endif
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nhwc_kyxc_nhwk_outpad<
BlockSize,
TInWei,
TAcc,
TOut,
E1,
E2,
KPerBlock,
HoPerBlock,
WoPerBlock,
EPerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K_E2,
ABlockTransferThreadClusterLengths_E0_E1_K_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K>{};
const auto ave_time =
conv_driver.Run(wei_k_y_x_c_desc,
in_n_hi_wi_c_desc,
out_n_ho_wo_k_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
out_n_ho_wo_k_device_buf.FromDevice(out_n_ho_wo_k.mData.data());
}
host/driver_offline/include/driver_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk.hpp deleted 100644 → 0
View file @ 17cd5c7d
#ifndef DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NHWC_KYXC_NHWK_HPP
#define DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::index_t E1_,
ck::index_t E2_,
ck::index_t KPerBlock,
ck::index_t HoPerBlock,
ck::index_t WoPerBlock,
ck::index_t E1PerBlock,
ck::index_t KPerThread,
ck::index_t HoPerThread,
ck::index_t WoPerThread,
ck::index_t EPerThread,
typename ABlockTransferThreadSliceLengths_E0_E1_K_E2,
typename ABlockTransferThreadClusterLengths_E0_E1_K_E2,
ck::index_t ABlockTransferSrcScalarPerVector_E2,
ck::index_t ABlockTransferDstScalarPerVector_E2,
ck::index_t BThreadTransferSrcScalarPerVector_E2,
ck::index_t CThreadTransferDstScalarPerVector_K>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nhwc_kyxc_nhwk_outpad
{
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ float Run(const ck::TensorDescriptor<Wei...>& wei_k_y_x_c_global_desc,
const ck::TensorDescriptor<In...>& in_n_hi_wi_c_global_desc,
const ck::TensorDescriptor<Out...>& out_n_ho_wo_k_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const int nrepeat) const
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_hi_wi_c_global_desc.GetLength(I0);
const auto Hi = in_n_hi_wi_c_global_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_global_desc.GetLength(I2);
const auto C = in_n_hi_wi_c_global_desc.GetLength(I3);
const auto Ho = out_n_ho_wo_k_global_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_global_desc.GetLength(I2);
const auto K = out_n_ho_wo_k_global_desc.GetLength(I3);
const auto Y = wei_k_y_x_c_global_desc.GetLength(I1);
const auto X = wei_k_y_x_c_global_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto Hop = (Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock;
const auto Wop = (Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock;
const auto OutRightPadH = Hop - Ho;
const auto OutRightPadW = Wop - Wo;
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0] + OutRightPadH * ConvStrideH;
const auto InRightPadW = in_right_pads[I1] + OutRightPadW * ConvStrideW;
std::cerr << "OutRightPadH = " << OutRightPadH << " OutRightPadW = " << OutRightPadW
<< std::endl;
std::cerr << "InRightPadH = " << InRightPadH << " InRightPadW = " << InRightPadW
<< std::endl;
constexpr auto E1 = Number<E1_>{};
constexpr auto E2 = Number<E2_>{};
const auto C0 = C / E2;
const auto E = Y * X * C0;
const auto E0 = E / E1;
// weight tensor
const auto a_e_k_e2_grid_desc =
transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, E, E2)),
make_tuple(make_pass_through_transform(K),
make_pass_through_transform(E),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}, Sequence<2>{}));
const auto a_e0_e1_k_e2_grid_desc =
transform_tensor_descriptor(a_e_k_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(K),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
// input tensor
const auto in_n_hip_wip_c0_e2_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C0, E2)),
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C0),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto in_n_y_ho_x_wo_c0_e2_global_desc = transform_tensor_descriptor(
in_n_hip_wip_c0_e2_global_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(Y, Hop), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wop), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C0),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}, Sequence<6>{}));
const auto b_e_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_n_y_ho_x_wo_c0_e2_global_desc,
make_tuple(make_merge_transform(make_tuple(Y, X, C0)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(
Sequence<1, 3, 5>{}, Sequence<0>{}, Sequence<2>{}, Sequence<4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto b_e0_e1_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
b_e_n_ho_wo_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}, Sequence<5>{}));
// output tensor
const auto c_k_n_hop_wop_grid_desc = transform_tensor_descriptor(
out_n_ho_wo_k_global_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Ho, 0, OutRightPadH),
make_pad_transform(Wo, 0, OutRightPadW),
make_pass_through_transform(K)),
make_tuple(Sequence<3>{}, Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
std::cerr << "Hop = " << Hop << " Wop = " << Wop << std::endl;
if(!((K % KPerBlock) == 0 && (Hop % HoPerBlock) == 0 && (Wop % WoPerBlock) == 0 &&
(E1 % E1PerBlock) == 0))
{
throw std::runtime_error("wrong! GEMM size no divisible");
}
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto a_e0_e1_k_e2_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto a_e0_e1_k_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0>{};
constexpr auto b_e0_e1_n_ho_wo_e2_global_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto b_e0_e1_n_ho_wo_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto c_k_n_ho_wo_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}));
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
decltype(a_e0_e1_k_e2_grid_desc),
decltype(b_e0_e1_n_ho_wo_e2_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
E1,
E2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K_E2,
ABlockTransferThreadClusterLengths_E0_E1_K_E2,
Sequence<0, 1, 2, 3>,
Sequence<0, 1, 2, 3>,
3,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 0, 1, 3, 4, 5>,
5,
BThreadTransferSrcScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy, which will be fused with
// MoveSrcSliceWindow() to save addr computation
Sequence<1, 2, 3, 0>,
0,
CThreadTransferDstScalarPerVector_K,
decltype(a_e0_e1_k_e2_global_step_hacks),
decltype(b_e0_e1_n_ho_wo_e2_global_step_hacks),
decltype(c_k_n_ho_wo_global_tensor_step_hacks),
decltype(a_e0_e1_k_e2_global_move_slice_window_step_hack),
decltype(b_e0_e1_n_ho_wo_e2_global_move_slice_window_step_hack)>;
using AGridDesc_E0_E1_K_E2 = decltype(a_e0_e1_k_e2_grid_desc);
using BGridDesc_E0_E1_N_Ho_Wo_E2 = decltype(b_e0_e1_n_ho_wo_e2_grid_desc);
using CGridDesc_K_N_Ho_Wo = decltype(c_k_n_hop_wop_grid_desc);
const auto grid_size = (K / KPerBlock) * (Hop / HoPerBlock) * (Wop / WoPerBlock) * N;
constexpr bool has_main_k_block_loop = (E1 + E1PerBlock) / (2 * E1PerBlock) > 1;
constexpr bool has_double_tail_k_block_loop = (E1 / E1PerBlock) % 2 == 0;
const bool has_e0_block_loop = E0 > 1;
std::cerr << "has_main_k_block_loop = " << has_main_k_block_loop
<< " has_double_tail_k_block_loop = " << has_double_tail_k_block_loop
<< " has_e0_block_loop = " << has_e0_block_loop << std::endl;
const auto c_blockid_to_k_n_ho_wo_block_cluster_adaptor =
make_single_stage_tensor_adaptor(make_tuple(make_pass_through_transform(I0)),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0>{}));
using CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo =
decltype(c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = 0;
if constexpr(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_e0_e1_k_e2_grid_desc,
b_e0_e1_n_ho_wo_e2_grid_desc,
c_k_n_hop_wop_grid_desc,
c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
}
else if constexpr(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_e0_e1_k_e2_grid_desc,
b_e0_e1_n_ho_wo_e2_grid_desc,
c_k_n_hop_wop_grid_desc,
c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
}
else if constexpr(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_e0_e1_k_e2_grid_desc,
b_e0_e1_n_ho_wo_e2_grid_desc,
c_k_n_hop_wop_grid_desc,
c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_e0_e1_k_e2_grid_desc,
b_e0_e1_n_ho_wo_e2_grid_desc,
c_k_n_hop_wop_grid_desc,
c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
}
return ave_time;
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_e0_e1_k_e2_grid_desc_dev_buf(sizeof(AGridDesc_E0_E1_K_E2));
DeviceMem b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf(sizeof(BGridDesc_E0_E1_N_Ho_Wo_E2));
DeviceMem c_k_n_hop_wop_grid_desc_dev_buf(sizeof(CGridDesc_K_N_Ho_Wo));
DeviceMem c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf(
sizeof(CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo));
a_e0_e1_k_e2_grid_desc_dev_buf.ToDevice(&a_e0_e1_k_e2_grid_desc);
b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf.ToDevice(&b_e0_e1_n_ho_wo_e2_grid_desc);
c_k_n_hop_wop_grid_desc_dev_buf.ToDevice(&c_k_n_hop_wop_grid_desc);
c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf.ToDevice(
&c_blockid_to_k_n_ho_wo_block_cluster_adaptor);
float ave_time = 0;
if constexpr(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
true,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_e0_e1_k_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_k_n_hop_wop_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if constexpr(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
true,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_e0_e1_k_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_k_n_hop_wop_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if constexpr(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
false,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_e0_e1_k_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_k_n_hop_wop_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else
{
const auto kernel =
kernel_gemm_dlops_v2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K_E2>,
remove_reference_t<BGridDesc_E0_E1_N_Ho_Wo_E2>,
remove_reference_t<CGridDesc_K_N_Ho_Wo>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_Ho_Wo>,
false,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_e0_e1_k_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_e0_e1_n_ho_wo_e2_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_k_n_hop_wop_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_k_n_ho_wo_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
return ave_time;
#endif
}
};
#endif
host/driver_offline/src/conv_fwd_driver_offline.cpp
View file @ 344707bb
...@@ -16,7 +16,6 @@ ...@@ -16,7 +16,6 @@
#include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
...@@ -24,7 +23,6 @@ ...@@ -24,7 +23,6 @@
#define USE_CONV_FWD_V4R4_NCHW 0 #define USE_CONV_FWD_V4R4_NCHW 0
#define USE_CONV_FWD_V4R4R2_NHWC 0 #define USE_CONV_FWD_V4R4R2_NHWC 0
#define USE_CONV_FWD_V6R1_NCHW 0 #define USE_CONV_FWD_V6R1_NCHW 0
#define USE_CONV_FWD_V5R1_NHWC 0
#define USE_CONV_FWD_V5R1_NCHWC 1 #define USE_CONV_FWD_V5R1_NCHWC 1
#define USE_CONV_FWD_V4R4R2_XDL_NCHW 0 #define USE_CONV_FWD_V4R4R2_XDL_NCHW 0
#define USE_CONV_FWD_V4R4R4_XDL_NHWC 0 #define USE_CONV_FWD_V4R4R4_XDL_NHWC 0
...@@ -35,9 +33,8 @@ enum ConvForwardAlgo ...@@ -35,9 +33,8 @@ enum ConvForwardAlgo
V4R4R2NHWC, // 1 V4R4R2NHWC, // 1
V6R1NCHW, // 2 V6R1NCHW, // 2
V5R1NCHWC, // 3 V5R1NCHWC, // 3
V5R1NHWC, // 4 V4R4R2XDLNCHW, // 4
V4R4R2XDLNCHW, // 5 V4R4R4XDLNHWC // 5
V4R4R4XDLNHWC // 6
}; };
int main(int argc, char* argv[]) int main(int argc, char* argv[])
...@@ -416,32 +413,6 @@ int main(int argc, char* argv[]) ...@@ -416,32 +413,6 @@ int main(int argc, char* argv[])
} }
#endif #endif
#if USE_CONV_FWD_V5R1_NHWC
if(algo == ConvForwardAlgo::V5R1NHWC)
{
if(layout != ConvTensorLayout::NHWC)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nhwc();
device_convolution_forward_implicit_gemm_v5r1_dlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4R2_XDL_NCHW #if USE_CONV_FWD_V4R4R2_XDL_NCHW
if(algo == ConvForwardAlgo::V4R4R2XDLNCHW) if(algo == ConvForwardAlgo::V4R4R2XDLNCHW)
{ {
......
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